Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes

Iztok Fister; Andres Iglesias; Akemi Galvez; Javier Del Ser; Eneko Osaba; Iztok Fister

doi:10.1007/978-3-030-03493-1_9

Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes

Iztok Fister^*, Andres Iglesias, Akemi Galvez, Javier Del Ser, Eneko Osaba, Iztok Fister^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Citations (Scopus)

Abstract

Association rule mining is a method for identification of dependence rules between features in a transaction database. In the past years, researchers applied the method using features consisting of categorical attributes. Rarely, numerical attributes were used in these studies. In this paper, we present a novel approach for mining association based on differential evolution, where features consist of numerical as well as categorical attributes. Thus, the problem is presented as a single objective optimization problem, where support and confidence of association rules are combined into a fitness function in order to determine the quality of the mined association rules. Initial experiments on sport data show that the proposed solution is promising for future development. Further challenges and problems are also exposed in this paper.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings
Editors	Hujun Yin, Paulo Novais, David Camacho, Antonio J. Tallón-Ballesteros
Publisher	Springer Verlag
Pages	79-88
Number of pages	10
ISBN (Print)	9783030034924
DOIs	https://doi.org/10.1007/978-3-030-03493-1_9
Publication status	Published - 2018
Event	19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 - Madrid, Spain Duration: 21 Nov 2018 → 23 Nov 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11314 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
Country/Territory	Spain
City	Madrid
Period	21/11/18 → 23/11/18

Funding

I. Fister Jr. and I. Fister acknowledge the financial support from the Slovenian Research Agency (Research Core Fundings No. P2-0041 and P2-0057). A. Iglesias and A. Galvez acknowledge the financial support from the projects #TIN2017-89275-R (AEI/FEDER, UE), and #JU12 (SODERCAN/FEDER UE). E. Osaba and J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK program. Acknowledgment. I. Fister Jr. and I. Fister acknowledge the financial support from the Slovenian Research Agency (Research Core Fundings No. P2-0041 and P2-0057). A. Iglesias and A. Galvez acknowledge the financial support from the projects #TIN2017-89275-R (AEI/FEDER, UE), and #JU12 (SODERCAN/FEDER UE). E. Osaba and J. Del Ser would like to thank the Basque Government for its funding support through the EMAITEK program.

Funders	Funder number
AEI/FEDER
SODERCAN/FEDER UE
Federación Española de Enfermedades Raras	#TIN2017-89275-R
Eusko Jaurlaritza
Javna Agencija za Raziskovalno Dejavnost RS	P2-0057, P2-0041

Keywords

Association rule mining
Classification
Differential evolution
Evolutionary computation

Access to Document

10.1007/978-3-030-03493-1_9

Cite this

Fister, I., Iglesias, A., Galvez, A., Del Ser, J., Osaba, E., & Fister, I. (2018). Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes. In H. Yin, P. Novais, D. Camacho, & A. J. Tallón-Ballesteros (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings (pp. 79-88). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11314 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-03493-1_9

Fister, Iztok ; Iglesias, Andres ; Galvez, Akemi et al. / Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes. Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. editor / Hujun Yin ; Paulo Novais ; David Camacho ; Antonio J. Tallón-Ballesteros. Springer Verlag, 2018. pp. 79-88 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Association rule mining is a method for identification of dependence rules between features in a transaction database. In the past years, researchers applied the method using features consisting of categorical attributes. Rarely, numerical attributes were used in these studies. In this paper, we present a novel approach for mining association based on differential evolution, where features consist of numerical as well as categorical attributes. Thus, the problem is presented as a single objective optimization problem, where support and confidence of association rules are combined into a fitness function in order to determine the quality of the mined association rules. Initial experiments on sport data show that the proposed solution is promising for future development. Further challenges and problems are also exposed in this paper.",

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Fister, I, Iglesias, A, Galvez, A, Del Ser, J , Osaba, E & Fister, I 2018, Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes. in H Yin, P Novais, D Camacho & AJ Tallón-Ballesteros (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11314 LNCS, Springer Verlag, pp. 79-88, 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018, Madrid, Spain, 21/11/18. https://doi.org/10.1007/978-3-030-03493-1_9

Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes. / Fister, Iztok; Iglesias, Andres; Galvez, Akemi et al.
Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. ed. / Hujun Yin; Paulo Novais; David Camacho; Antonio J. Tallón-Ballesteros. Springer Verlag, 2018. p. 79-88 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11314 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes

AU - Fister, Iztok

AU - Iglesias, Andres

AU - Galvez, Akemi

AU - Del Ser, Javier

AU - Osaba, Eneko

AU - Fister, Iztok

PY - 2018

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N2 - Association rule mining is a method for identification of dependence rules between features in a transaction database. In the past years, researchers applied the method using features consisting of categorical attributes. Rarely, numerical attributes were used in these studies. In this paper, we present a novel approach for mining association based on differential evolution, where features consist of numerical as well as categorical attributes. Thus, the problem is presented as a single objective optimization problem, where support and confidence of association rules are combined into a fitness function in order to determine the quality of the mined association rules. Initial experiments on sport data show that the proposed solution is promising for future development. Further challenges and problems are also exposed in this paper.

AB - Association rule mining is a method for identification of dependence rules between features in a transaction database. In the past years, researchers applied the method using features consisting of categorical attributes. Rarely, numerical attributes were used in these studies. In this paper, we present a novel approach for mining association based on differential evolution, where features consist of numerical as well as categorical attributes. Thus, the problem is presented as a single objective optimization problem, where support and confidence of association rules are combined into a fitness function in order to determine the quality of the mined association rules. Initial experiments on sport data show that the proposed solution is promising for future development. Further challenges and problems are also exposed in this paper.

KW - Association rule mining

KW - Classification

KW - Differential evolution

KW - Evolutionary computation

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DO - 10.1007/978-3-030-03493-1_9

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A2 - Camacho, David

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PB - Springer Verlag

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ER -

Fister I, Iglesias A, Galvez A, Del Ser J , Osaba E, Fister I. Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes. In Yin H, Novais P, Camacho D, Tallón-Ballesteros AJ, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings. Springer Verlag. 2018. p. 79-88. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-03493-1_9

Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes

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